Our proposed solution model, UWMMSE, is composed of 4 unrolled WMMSE layers with each layer \(k\)  having two 2-layered GCNs (REF )-() as its learning modules for the affine transform parameters \({\mathbf {a}}^{(k)}\)  and \({\mathbf {b}}^{(k)}\) .
Hidden layer dimension of all GCNs is set to 4.
ADAM [1]} optimizer is employed for training across 10000 steps with each training step being performed on a batch of 64 randomly sampled channel fading states.
Learning rate is set to 1e-3 and training is performed for a maximum of 20 epochs, with early stopping.
For testing, we randomly sample 6400 channel fading states. Unless otherwise specified, the network size \(M\)  is fixed at 20.
